lsp-architecture
About
This Claude Skill generates a comprehensive architectural overview of a codebase using LSP analysis. It provides the big picture by identifying languages, package structure, entry points, dependency graphs, and hotspots in a single call. It's ideal for developers needing to quickly understand a new or complex project's structure without modifying files.
Quick Install
Claude Code
Recommendednpx skills add blackwell-systems/agent-lsp -a claude-code/plugin add https://github.com/blackwell-systems/agent-lspgit clone https://github.com/blackwell-systems/agent-lsp.git ~/.claude/skills/lsp-architectureCopy and paste this command in Claude Code to install this skill
Documentation
Requires the agent-lsp MCP server.
lsp-architecture
Generate a structural architecture overview of any codebase: language distribution, package hierarchy, entry points, dependency flow, and hotspot files. One invocation for the big picture.
Read-only; does not modify any files.
Invocation: User provides the workspace root path (e.g.
"/home/user/myproject"). If omitted, use the current working directory.
Depth Controls (hard limits)
These limits are strict constraints. Never exceed them:
- Package enumeration: cap at 30 packages
- Hotspot analysis: cap at 10 files
- Workspace symbol queries: cap at 5 queries
- Do NOT recurse into
vendor/,node_modules/,.git/, or other dependency directories
Step 0 — Initialize
If LSP is not yet running, start it with the workspace root:
mcp__lsp__start_lsp({
"workspace_root": "<workspace-root>"
})
Then detect which language servers are available:
mcp__lsp__detect_lsp_servers({
"workspace_root": "<workspace-root>"
})
→ returns: list of detected servers with language names and file patterns
Record the available languages and their file globs. This determines which queries to run in later steps.
Step 1 — Language Detection
Scan the workspace to determine language distribution. Use file extension counts from the detected servers and supplement with a filesystem scan (via Glob tool) to count files per language.
For each detected language, report:
- Language name
- File count
- Estimated lines of code (sample 3-5 representative files and extrapolate)
Skip files in vendor/, node_modules/, .git/, dist/, build/, and
other common dependency or output directories.
Step 2 — Package Structure
Use find_symbol with broad queries to discover the package and
module hierarchy. Tailor queries by language:
Go:
mcp__lsp__find_symbol({
"query": "",
"symbol_kind_filter": "Package"
})
Also query for top-level types and functions to fill in package-level detail:
mcp__lsp__find_symbol({
"query": "",
"symbol_kind_filter": "Function"
})
Python:
mcp__lsp__find_symbol({
"query": "",
"symbol_kind_filter": "Class"
})
TypeScript/JavaScript:
mcp__lsp__find_symbol({
"query": "",
"symbol_kind_filter": "Function"
})
From the returned symbols, extract the directory paths and build a tree of the package hierarchy. Group symbols by their containing directory. For each package/directory, note:
- Path relative to workspace root
- Brief description (inferred from symbol names and directory name)
- Approximate symbol count
Cap at 30 packages. If more than 30 directories contain symbols, keep only the top 30 by symbol count and note that others were omitted.
Cap workspace symbol queries at 5 total across all of Step 2.
Step 3 — Entry Points
Use find_symbol to search for common entry point patterns:
mcp__lsp__find_symbol({
"query": "main"
})
Also search for other common entry point names (use a single additional query
if needed): "Run", "Serve", "Handler", "App", "Main".
Identify and categorize:
- CLI entrypoints:
mainfunctions,RunorExecutecommands - HTTP handlers:
Handler,Serve,ListenAndServepatterns - Test suites: top-level test files (note count only, do not list individually)
List each entry point with file:line.
Step 4 — Hotspot Analysis
Identify the files with the highest symbol density (most exports), then measure their blast radius.
4a — Find candidate files
From the symbols discovered in Step 2, count exported symbols per file. Select the top 10 files by exported symbol count.
4b — Measure blast radius
For each candidate file, call blast_radius:
mcp__lsp__blast_radius({
"changed_files": ["<absolute-path-to-file>"],
"include_transitive": false
})
→ returns: affected_symbols, test_callers, non_test_callers
This tool uses a persistent cache, so repeated calls on the same file are instant.
4c — Rank hotspots
Rank files by total non_test_callers count (descending). Files with the
most non-test callers are the architectural hotspots: changing them has the
widest blast radius.
Step 5 — Output
Produce the architecture report in this format:
## Architecture Overview: <project-name>
### Languages
- Go: 150 files (~15K lines)
- TypeScript: 30 files (~3K lines)
### Package Map
cmd/agent-lsp/ (entrypoint, CLI routing)
internal/lsp/ (LSP client, process management)
internal/tools/ (MCP tool handlers)
internal/session/ (speculative execution sessions)
...
### Entry Points
- cmd/agent-lsp/main.go:55 (main)
- cmd/agent-lsp/server.go:276 (Run)
### Hotspots (most referenced files)
1. internal/lsp/client.go: 150+ callers across 30 files
2. internal/tools/helpers.go: 80 callers across 20 files
...
### Dependency Flow
cmd/ -> internal/tools/ -> internal/lsp/ -> (gopls subprocess)
|-> internal/session/ -> internal/lsp/
Report sections
Languages: One line per language with file count and estimated LOC.
Package Map: Directory tree with a parenthetical description of each package's role. Cap at 30 entries.
Entry Points: Each with file:line and a parenthetical label (e.g.
(main), (HTTP handler), (CLI command)).
Hotspots: Ranked list of the most-referenced files. For each, show the total non-test caller count and the number of distinct files containing callers.
Dependency Flow: A simple ASCII arrow diagram showing how the top-level packages depend on each other. Infer this from the hotspot caller data and package structure. Keep it concise: show the primary flow paths, not every edge.
Example
Goal: architecture overview of /home/user/agent-lsp
Step 0 — Initialize
start_lsp: workspace_root="/home/user/agent-lsp"
detect_lsp_servers:
→ Go (gopls): *.go
→ detected 1 language server
Step 1 — Language Detection
Glob: **/*.go (excluding vendor/) → 85 files
Sample 5 files, average 180 lines → estimate ~15K total lines
→ Go: 85 files (~15K lines)
Step 2 — Package Structure
find_symbol: query="", symbol_kind_filter="Package"
→ 12 packages found
find_symbol: query="", symbol_kind_filter="Function"
→ 240 functions across 12 packages
Package map:
cmd/agent-lsp/ (entrypoint, CLI routing, 15 symbols)
internal/lsp/ (LSP client lifecycle, 45 symbols)
internal/tools/ (MCP tool handlers, 60 symbols)
internal/session/ (speculative execution, 25 symbols)
internal/protocol/ (LSP protocol types, 30 symbols)
skills/ (embedded skill definitions, 5 symbols)
Step 3 — Entry Points
find_symbol: query="main"
→ cmd/agent-lsp/main.go:55 main()
find_symbol: query="Run"
→ cmd/agent-lsp/server.go:276 Run()
→ cmd/agent-lsp/daemon.go:40 RunDaemon()
Entry points:
- cmd/agent-lsp/main.go:55 (main)
- cmd/agent-lsp/server.go:276 (Run, server lifecycle)
- cmd/agent-lsp/daemon.go:40 (RunDaemon, background mode)
Step 4 — Hotspot Analysis
Top files by symbol count:
1. internal/lsp/client.go (22 exported symbols)
2. internal/tools/helpers.go (18 exported symbols)
3. internal/protocol/types.go (15 exported symbols)
blast_radius on each:
internal/lsp/client.go → 150 non-test callers across 30 files
internal/tools/helpers.go → 80 non-test callers across 20 files
internal/protocol/types.go → 60 non-test callers across 15 files
Step 5 — Output
## Architecture Overview: agent-lsp
### Languages
- Go: 85 files (~15K lines)
### Package Map
cmd/agent-lsp/ (entrypoint, CLI routing)
internal/lsp/ (LSP client, process management)
internal/tools/ (MCP tool handlers)
internal/session/ (speculative execution sessions)
internal/protocol/ (LSP protocol types)
skills/ (embedded skill definitions)
### Entry Points
- cmd/agent-lsp/main.go:55 (main)
- cmd/agent-lsp/server.go:276 (Run)
- cmd/agent-lsp/daemon.go:40 (RunDaemon)
### Hotspots (most referenced files)
1. internal/lsp/client.go: 150 callers across 30 files
2. internal/tools/helpers.go: 80 callers across 20 files
3. internal/protocol/types.go: 60 callers across 15 files
### Dependency Flow
cmd/agent-lsp/ -> internal/tools/ -> internal/lsp/ -> (gopls subprocess)
|-> internal/session/ -> internal/lsp/
|-> internal/protocol/
GitHub Repository
Frequently asked questions
What is the lsp-architecture skill?
lsp-architecture is a Claude Skill by blackwell-systems. Skills package instructions and resources that Claude loads on demand, so Claude can perform lsp-architecture-related tasks without extra prompting.
How do I install lsp-architecture?
Use the install commands on this page: add lsp-architecture to Claude Code as a plugin, or clone its repository into your skills directory, then restart Claude so it picks up the skill.
What category does lsp-architecture belong to?
lsp-architecture is in the Meta category.
Is lsp-architecture free to use?
Yes. lsp-architecture is listed on AIMCP and free to install.
Related Skills
This skill provides a production-tested setup for Content Collections, a TypeScript-first tool that transforms Markdown/MDX files into type-safe data collections with Zod validation. Use it when building blogs, documentation sites, or content-heavy Vite + React applications to ensure type safety and automatic content validation. It covers everything from Vite plugin configuration and MDX compilation to deployment optimization and schema validation.
This skill enables developers to build applications with the Polymarket prediction markets platform, including API integration for trading and market data. It also provides real-time data streaming via WebSocket to monitor live trades and market activity. Use it for implementing trading strategies or creating tools that process live market updates.
This skill helps developers create OpenCode plugins that hook into 25+ event types like commands, files, and LSP operations. It provides the plugin structure, event API specifications, and implementation patterns for JavaScript/TypeScript modules. Use it when you need to intercept, monitor, or extend the OpenCode AI assistant's lifecycle with custom event-driven logic.
SGLang is a high-performance LLM serving framework that specializes in fast, structured generation for JSON, regex, and agentic workflows using its RadixAttention prefix caching. It delivers significantly faster inference, especially for tasks with repeated prefixes, making it ideal for complex, structured outputs and multi-turn conversations. Choose SGLang over alternatives like vLLM when you need constrained decoding or are building applications with extensive prefix sharing.
